Resource reservation in wireless networks based on pattern recognition

Abstract

Resource Reservation is very important for handoff control in wireless networks. Now many researches have aimed to predict the user's destination cell based on its movement pattern for efficient resource reservation. In the future networks with small size cells, handoffs will occur more frequently and the user's movement will be more like random processes, so it is not practical to predict the accurate destination of a user. We propose a statistical strategy for resource reservation through the estimation of a user's Transfer Probabilities, which represent the possibilities of the user leaving the current cell and entering the neighboring cells. The resources reserved for a user in each base station are proportional to the user's Transfer Probabilities. A mathematical model is proposed to obtain the Transfer Probabilities of a user from the initial states (position, velocity and direction) through simulation of the user's movement. Neural Networks are developped to predict the Transfer Probabilities of a user from the initial states and facilitate efflcient resource reservation.

A Neural Network Demand Prediction Scheme for Resource Allocation in Cellular Wireless Systems

“…Our model, along with those proposed by [7], [12] is data driven. Since service providers are reluctant to provide the required data, we use comparative models based on synthesized data derived from simulation models.…”

“…Dynamic bandwidth reservation schemes assign bandwidth from a reservation pool based on predicted usage at each cell or sector [9], [1], [10], [11], [12], [13], [14]. Some schemes involve simple thresholds to determine if bandwidth is needed.…”

Survey on artificial intelligence based techniques for emerging robotic communication

“…IoRT Sharma et al [61,35,40] Narasimhan et al [56] Salamat et al [70] Yu et al [58] Li et al [27] Guzey et al [24] Hauert et al [73] Min et al [75] Akat et al [76] Razafimandimby et al [66] Vermesan et al [82] Gerla et al [84] Al-Sakran et al [85] Aerospace Robotic Ground…”

“…Adaptive parameters such as user action speed, receive signal strength for pattern classification provide a multiple of criteria hand-off algorithm [57]. The neural network was trained to predict a user's transfer probabilities which represented the user movements [58]. ANN has been utilized to enhance hand-off techniques for auto-driving due to its ability to handle large data.…”

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“…Brand visual identity shapes consumer perceptions, emotions, and behaviors. Elements such as brand logo [8,32], colour [33], name [34][35][36], layout design [37], fonts [38], and other visual components have been proven to significantly affect brand satisfaction [39,40], consumer attitudes such as loyalty [25,41,42], favorability [43], and awareness [44], as well as purchase intentions [45] and social attitudes [46]. Understanding the impact of a brand's visual identity on consumer attitudes is critical for marketers and brand managers to effectively manage [47] and leverage these visual elements to create positive consumer experiences [44,48,49], and perceptions.…”